Unlocking Neural Transparency: Jacobian Maps for Explainable AI in Alzheimer's Detection
This addresses the need for trustworthy AI in clinical settings for Alzheimer's detection, though it appears incremental as it builds on existing explainability techniques like Grad-CAM.
The paper tackled the problem of limited interpretability in deep learning models for Alzheimer's disease detection by introducing Jacobian Maps (JMs) as a pre-model approach, which demonstrated superior accuracy compared to traditional methods and provided improved interpretability through correlations with neuroanatomical biomarkers.
Alzheimer's disease (AD) leads to progressive cognitive decline, making early detection crucial for effective intervention. While deep learning models have shown high accuracy in AD diagnosis, their lack of interpretability limits clinical trust and adoption. This paper introduces a novel pre-model approach leveraging Jacobian Maps (JMs) within a multi-modal framework to enhance explainability and trustworthiness in AD detection. By capturing localized brain volume changes, JMs establish meaningful correlations between model predictions and well-known neuroanatomical biomarkers of AD. We validate JMs through experiments comparing a 3D CNN trained on JMs versus on traditional preprocessed data, which demonstrates superior accuracy. We also employ 3D Grad-CAM analysis to provide both visual and quantitative insights, further showcasing improved interpretability and diagnostic reliability.